Using the ABCT Analysis Pipeline
Introduction
This vignette demonstrates how to identify malignant cells and perform anchor-based cell type classification using the CosMx SMI NSCLC FFPE Dataset, Lung 5-1 sample. We employ the FindMalignantCells function to detect malignant cells based on predefined marker genes and subsequently apply RunABCT for cell type annotation using an anchor-based classification approach. This workflow showcases the integration of spatial transcriptomics data with computational methods for accurate cell type identification.
Loading Required Libraries
Ensure that all necessary libraries are installed and loaded.
library(Seurat)
library(SeuratWrappers)
library(Banksy)
library(harmony)
library(dplyr)
library(scales)
library(EnvStats)
library(stringr)
library(matrixStats)
library(InSituType)
library(UCell)
library(ggplot2)
library(MASS)
library(pracma)
source("./ABCT.r")Loading and Preparing Data
Load the data for analysis. Here, we demonstrate using a Seurat object. The processed data can be downloaded from https://kbds.re.kr/hissta/datasetinfo?sampleIdx=19.
obj <- readRDS(paste0("/path/to/your/data/", "obj_final.rds"))Define Cell Type List and Marker Genes
We define a list of cell types and specify marker genes for the analysis.
celltype_list <- c("Malignant", "Epithelial", "CD4T", "CD8T", "NK", "B", "Plasma",
"Macrophage", "Monocyte", "DC", "Mast", "Neutrophil",
"Endothelial", "Fibroblast", "Unknown")
names(celltype_list) <- c(
"#bf4040", "#FB8072", "#8DD3C7", "#FFFFB3", "#BEBADA", "#3288bd",
"#FDB462", "#B3DE69", "#FCCDE5", "#84cdee", "#BC80BD", "#CCEBC5",
"#FFED6F", "#1b9e77", "gray"
)
malignant_marker_list <- data.frame(
cluster = "Malignant",
gene = c("SOX9", "FGFR1", "KRAS", "MYC", "EGFR")
)
ABCT_marker_list <- read.csv("/path/to/marker_list.csv")
ABCT_marker_list$cluster <- factor(ABCT_marker_list$cluster, levels = celltype_list)
ABCT_marker_list <- ABCT_marker_list %>% arrange(cluster)Identifying Malignant Cells
Use the FindMalignantCells function to identify
malignant cells in the data.
malignant_path <- "/path/to/output/malignant/"
obj <- FindMalignantCells(
obj,
assay = "SCT",
ctrl_assay = "negprobes",
marker_list = malignant_marker_list,
use_spatial = TRUE,
M = 1,
lambda = 0.2,
area = NULL,
w_neg = 0,
dimx = "x_global_px",
dimy = "y_global_px",
smooth_reduction = "spatial_pca",
path = malignant_path
)
#> Malignant:
#> SOX9, FGFR1, KRAS, MYC, EGFR, SOX9.m0, FGFR1.m0, KRAS.m0, MYC.m0, EGFR.m0
#> Smoothing module score (UCell)
#> npcs: 30
#> Saving 10 x 5 in image
#> Coordinate system already present. Adding new coordinate system, which will replace
#> the existing one.
Performing ABCT Classification
Subsequently, classify the non-malignant cells using the RunABCT function. By default, if a BANKSY object already exists in the Seurat object, RunABCT will use the existing BANKSY object rather than running it again. If you wish to rerun BANKSY on a subsetted object, you should remove the existing BANKSY assay before executing the function.
subobj <- subset(obj, subset = malignant_result != "Malignant")
#> Warning: Not validating FOV objects
#> Warning: Not validating Centroids objects
#> Not validating Centroids objects
#> Warning: Not validating FOV objects
#> Not validating FOV objects
#> Not validating FOV objects
#> Warning: Not validating Seurat objects
abct_path <- "/path/to/output/abct/"
subobj <- RunABCT(
subobj,
assay = "SCT",
ctrl_assay = "negprobes",
marker_list = ABCT_marker_list,
color_list = names(celltype_list[celltype_list %in% ABCT_marker_list$cluster]),
method = "quantile",
use_spatial = TRUE,
M = 1,
lambda = 0.2,
dimx = "x_global_px",
dimy = "y_global_px",
smooth_reduction = "spatial_pca",
path = abct_path
)
#> Calculating UCell module score
#> Epithelial:
#> KRT7, KRT8, KRT19, EPCAM, PIGR, KRT7.m0, KRT8.m0, KRT19.m0, EPCAM.m0, PIGR.m0
#> CD4T:
#> CD2, CD3D, CD3E, CD3G, CD4, LTB, IL7R, CD2.m0, CD3D.m0, CD3E.m0, CD3G.m0, CD4.m0, LTB.m0, IL7R.m0
#> CD8T:
#> CD2, CD3D, CD3E, CD3G, CD8A, CD8B, CCL5, CD2.m0, CD3D.m0, CD3E.m0, CD3G.m0, CD8A.m0, CD8B.m0, CCL5.m0
#> NK:
#> NKG7, GNLY, KLRB1, PRF1, NKG7.m0, GNLY.m0, KLRB1.m0, PRF1.m0
#> B:
#> MS4A1, CD79A, CD19, CD37, CD38, MS4A1.m0, CD79A.m0, CD19.m0, CD37.m0, CD38.m0
#> Plasma:
#> MZB1, JCHAIN, TNFRSF17, IGHG1, MZB1.m0, JCHAIN.m0, TNFRSF17.m0, IGHG1.m0
#> Macrophage:
#> CD68, C1QA, C1QC, C1QB, CD68.m0, C1QA.m0, C1QC.m0, C1QB.m0
#> Monocyte:
#> S100A8, S100A9, LYZ, CD14, VCAN, S100A8.m0, S100A9.m0, LYZ.m0, CD14.m0, VCAN.m0
#> DC:
#> HLA-DQA1, HLA-DQB1, HLA-DPB1, HLA-DPA1, CLEC10A, GPR183, ITGAX, CD86, HLA-DQA1.m0, HLA-DQB1.m0, HLA-DPB1.m0, HLA-DPA1.m0, CLEC10A.m0, GPR183.m0, ITGAX.m0, CD86.m0
#> Mast:
#> CPA3, TPSB2, TPSAB1, KIT, CPA3.m0, TPSB2.m0, TPSAB1.m0, KIT.m0
#> Neutrophil:
#> CSF3R, CXCR2, CXCL8, CSF3R.m0, CXCR2.m0, CXCL8.m0
#> Endothelial:
#> PECAM1, VWF, PECAM1.m0, VWF.m0
#> Fibroblast:
#> DCN, COL1A1, COL1A2, LUM, PDGFRA, DCN.m0, COL1A1.m0, COL1A2.m0, LUM.m0, PDGFRA.m0
#> ** CellType max score result **
#>
#> Epithelial CD4T CD8T NK B Plasma
#> 5059 5133 295 54 4568 5140
#> Macrophage Monocyte DC Mast Neutrophil Endothelial
#> 19494 1664 2709 2369 3440 6295
#> Fibroblast <NA>
#> 23363 0
#> * Epithelial anchor
#> anchor
#> Epithelial filter
#> 1012 78571
#> * CD4T anchor
#> anchor
#> CD4T filter
#> 1027 78556
#> * CD8T anchor
#> anchor
#> CD8T filter
#> 59 79524
#> * NK anchor
#> anchor
#> filter NK
#> 79572 11
#> * B anchor
#> anchor
#> B filter
#> 914 78669
#> * Plasma anchor
#> anchor
#> filter Plasma
#> 78555 1028
#> * Macrophage anchor
#> anchor
#> filter Macrophage
#> 75684 3899
#> * Monocyte anchor
#> anchor
#> filter Monocyte
#> 79250 333
#> * DC anchor
#> anchor
#> DC filter
#> 542 79041
#> * Mast anchor
#> anchor
#> filter Mast
#> 79109 474
#> * Neutrophil anchor
#> anchor
#> filter Neutrophil
#> 78895 688
#> * Endothelial anchor
#> anchor
#> Endothelial filter
#> 1259 78324
#> * Fibroblast anchor
#> anchor
#> Fibroblast filter
#> 4673 74910
#> Final anchor
#> merged_anchor
#> B CD4T CD8T DC Endothelial Epithelial
#> 914 1027 59 542 1259 1012
#> Fibroblast filter Macrophage Mast Monocyte Neutrophil
#> 4673 63664 3899 474 333 688
#> NK Plasma <NA>
#> 11 1028 0
#> Running Insitutype
#> Probability cutoff: 0.99
#> Anchor result (clust)
#>
#> B CD4T CD8T DC Endothelial Epithelial
#> 6864 10296 1640 4142 6351 4522
#> Fibroblast Macrophage Mast Monocyte Neutrophil NK
#> 15761 8272 2401 3422 8837 252
#> Plasma
#> 6823
#> Anchor result (cut)
#>
#> B CD4T CD8T DC Endothelial Epithelial
#> 6313 9043 1252 3433 6028 4240
#> Fibroblast Macrophage Mast Monocyte Neutrophil NK
#> 15143 7753 2279 2607 8306 182
#> Plasma Unknown
#> 6597 6407
#> Saving annotation result as dataframe (csv)
#> Saving annotation result as rds object
#> Coordinate system already present. Adding new coordinate system, which will replace
#> the existing one.
#> Coordinate system already present. Adding new coordinate system, which will replace
#> the existing one.
Updating Metadata
Finally, update the metadata in the original object with the ABCT classification results.
obj <- update_metadata(obj, subobj, celltype_list)
Session Info
#> R version 4.3.2 (2023-10-31)
#> Platform: x86_64-conda-linux-gnu (64-bit)
#> Running under: Rocky Linux 8.10 (Green Obsidian)
#>
#> Matrix products: default
#>
#> locale:
#> [1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
#> [3] LC_TIME=ko_KR.UTF-8 LC_COLLATE=en_US.UTF-8
#> [5] LC_MONETARY=ko_KR.UTF-8 LC_MESSAGES=en_US.UTF-8
#> [7] LC_PAPER=ko_KR.UTF-8 LC_NAME=C
#> [9] LC_ADDRESS=C LC_TELEPHONE=C
#> [11] LC_MEASUREMENT=ko_KR.UTF-8 LC_IDENTIFICATION=C
#>
#> time zone: Asia/Seoul
#> tzcode source: system (glibc)
#>
#> attached base packages:
#> [1] stats graphics grDevices utils datasets methods base
#>
#> other attached packages:
#> [1] pracma_2.4.4 MASS_7.3-60 ggplot2_3.5.1
#> [4] UCell_2.6.2 InSituType_2.0 matrixStats_1.3.0
#> [7] stringr_1.5.1 EnvStats_2.8.1 scales_1.3.0
#> [10] dplyr_1.1.4 harmony_1.2.0 Rcpp_1.0.12
#> [13] Banksy_0.99.13 SeuratWrappers_0.3.5 Seurat_5.0.1
#> [16] SeuratObject_5.0.1 sp_2.1-4
#>
#> loaded via a namespace (and not attached):
#> [1] RcppHungarian_0.3 RcppAnnoy_0.0.22
#> [3] splines_4.3.2 later_1.3.2
#> [5] bitops_1.0-7 tibble_3.2.1
#> [7] R.oo_1.26.0 polyclip_1.10-6
#> [9] fastDummies_1.7.3 lifecycle_1.0.4
#> [11] aricode_1.0.3 globals_0.16.3
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#> [15] magrittr_2.0.3 plotly_4.10.4.9000
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#> [19] jquerylib_0.1.4 yaml_2.3.8
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#> [25] spam_2.10-0 spatstat.sparse_3.0-3
#> [27] reticulate_1.37.0 cowplot_1.1.3
#> [29] pbapply_1.7-2 RColorBrewer_1.1-3
#> [31] abind_1.4-5 zlibbioc_1.48.0
#> [33] Rtsne_0.17 GenomicRanges_1.54.1
#> [35] purrr_1.0.2 R.utils_2.12.3
#> [37] BiocGenerics_0.48.1 RCurl_1.98-1.14
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#> [125] cluster_2.1.6 evaluate_0.23
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